Typically audio repair and restoration techniques are applied to pre-recorded audio to remove imperfections such as hiss, crackle, noise and buzz from the audio while still retaining as much of the quality and authenticity of the original recording as possible. For example, audio restoration may be used to clean up an old vinyl record which has degraded over time. The vinyl record may have acquired various scratches and imperfections, and converting the recording to a digital medium such as an MP3 results in these imperfections also being transferred. FIG. 1 shows the harmonic waveform of a prior art audio recording which contains several disturbance events 10. These disturbance events can clearly be seen as large peaks in the amplitude which extend above the amplitude of normal harmonic waves 12.
FIG. 2 shows a prior art example of an amplitude plot of a waveform that includes a mixture of disturbance events 10. The disturbance types are broadly speaking characterized into 3 main categories, namely, pops, clicks and crackles. A pop 10a has a large amplitude and is typically 2 ms or longer in duration. A click 10b has a smaller amplitude and is shorter in duration, typically around 0.3 to 1.0 ms. Clicks don't tend to obliterate the underlying signal, but they are still audible to the listener. Crackles 10c are even smaller in amplitude and are less than 0.3 ms in duration. The crackles are often heard as persistent background noise. FIG. 3 shows an expanded view of a portion of the amplitude plot of FIG. 2 in which a click 10c is identified.
Prior art audio repair and restoration techniques work by streaming a sample of audio into a predictor algorithm which attempts to follow the harmonic profile of the signal. The predictor algorithm looks at a stream of samples and is then able to identify within a certain degree of error where the following samples in the stream will lie in amplitude. A profile may be modelled by the predictor algorithm and such a modelled profile may then be used to identify disturbance events 10 by comparing the actual harmonic profile with that predicted by the algorithm. Significant deviances from the predicted profile are identified as disturbances. It should be noted that the algorithm determines which events are classified as pops, clicks and crackles based on their harmonic profile and that these above distinctions are merely a general classification.
One of the problems associated with the use of such predictor algorithms is distinguishing events caused by the natural harmonics of certain types of audio from genuine distortion events. Brass music in particular is known to be difficult for the predictor algorithms to accurately model. In these cases registered events are typically not caused by disturbance, but are inherent and vital to the character of the brass music. Repairing these events makes the resultant music sound dull and affects the integrity of the sound.
Another similar problem associated with the known use of predictor algorithms is that the user is not easily able to select which portions of recorded audio should be repaired and which should not.
Cleaning up audio recordings that are in the form of dialogue or which include sections of dialogue also creates additional problems. Different sets of parameters are often required during speech compared to those that are required for the pauses between speaking. Setting the parameters too aggressively means that many of the natural harmonics of the recorded voice would be repaired thus affecting the sound quality. In this case it may be preferable to use lower settings because the dialogue masks the disturbance events. However, in such a case much of the background noise would escape repair and this would be particularly exposed during the pauses. One known method for overcoming this is by automation. In this way someone manually goes through the recording to determine which events are speech and which are not, and they set the parameters accordingly. This approach, however, is laborious and prone to errors.